Manjushri commited on
Commit
f3f2d57
1 Parent(s): b54ffb0

Update app.py

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Files changed (1) hide show
  1. app.py +6 -7
app.py CHANGED
@@ -3,11 +3,11 @@ import modin.pandas as pd
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  import torch
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  import numpy as np
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  from PIL import Image
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- from diffusers import DiffusionPipeline
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-
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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- pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo")
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  pipe = pipe.to(device)
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  def resize(value,img):
@@ -15,17 +15,16 @@ def resize(value,img):
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  img = img.resize((value,value))
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  return img
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- def infer(source_img, prompt, steps, seed):
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  generator = torch.Generator(device).manual_seed(seed)
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  source_image = resize(512, source_img)
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  source_image.save('source.png')
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- image = pipe(prompt, image=source_image, strength=0.5, guidance_scale=0.0, num_inference_steps=steps).images[0]
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  return image
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  gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"),
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  gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'),
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- #gr.Slider(2, 15, value = 7, label = 'Guidance Scale'),
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  gr.Slider(1, 5, value = 2, step = 1, label = 'Number of Iterations'),
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  gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True),
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- #gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)],
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  outputs='image', title = "Stable Diffusion XL 1.0 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()
 
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  import torch
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  import numpy as np
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  from PIL import Image
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+ from diffusers import AutoPipelineForImage2Image
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+ from diffusers.utils import load_image
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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+ pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16) if torch.cuda.is_available() else AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo")
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  pipe = pipe.to(device)
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  def resize(value,img):
 
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  img = img.resize((value,value))
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  return img
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+ def infer(source_img, prompt, steps, seed, Strength):
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  generator = torch.Generator(device).manual_seed(seed)
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  source_image = resize(512, source_img)
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  source_image.save('source.png')
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+ image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps).images[0]
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  return image
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  gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"),
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  gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'),
 
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  gr.Slider(1, 5, value = 2, step = 1, label = 'Number of Iterations'),
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  gr.Slider(label = "Seed", minimum = 0, maximum = 987654321987654321, step = 1, randomize = True),
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+ gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5)],
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  outputs='image', title = "Stable Diffusion XL 1.0 Image to Image Pipeline CPU", description = "For more information on Stable Diffusion XL 1.0 see https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0 <br><br>Upload an Image (<b>MUST Be .PNG and 512x512 or 768x768</b>) enter a Prompt, or let it just do its Thing, then click submit. 10 Iterations takes about ~900-1200 seconds currently. For more informationon about Stable Diffusion or Suggestions for prompts, keywords, artists or styles see https://github.com/Maks-s/sd-akashic", article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").queue(max_size=5).launch()